Artificial Intelligence and Computational Approaches for Epilepsy

Artificial Intelligence and Multiple Sclerosis

In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.

Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population

PURPOSE

This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE).

METHODS

Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into 'good responder (GR)' and 'poor responder (PR)' based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to construct classification models.

RESULTS

Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chi-square (χ2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furthermore, our surrogate analysis revealed that the null hypothesis for both XGB and SVC was rejected at a 100 % confidence level, underscoring the significance of their predictive performance. These findings underscore the robustness and reliability of XGB and SVC in our predictive modelling framework.

SIGNIFICANCE

Utilizing XG Boost and SVC-based machine learning classifier, we successfully forecasted the likelihood of a patient's response to ASM treatment, categorizing them as either PR or GR, post-completion of standard epilepsy examinations. The classifier's predictions were found to be statistically significant, suggesting their potential utility in improving treatment strategies, particularly in the personalized selection of ASM regimens for individual epilepsy patients.

Evaluations of artificial intelligence and machine learning algorithms in neurodiagnostics

This article evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI/machine learning (ML) algorithms are analyzed, as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models. As ANN and DNN analyses can be applied to data with an unknown clinical diagnosis, these algorithms are evaluated according to Bayesian statistical analyses. Bayesian neural network analyses are incorporated, as these algorithms indicate that the predictive accuracy and model performance are dependent upon accurate configurations of the model's hyperparameters and neural inputs. Thus, mathematical evaluations of AI algorithms are comprehensively explored to examine their clinical utility, as underperformance of AI/ML models may have deleterious consequences that affect patient outcomes due to misdiagnosis and false-negative test results.

A decision support system based on recurrent neural networks to predict medication dosage for patients with Parkinson's disease

Using deep learning has demonstrated significant potential in making informed decisions based on clinical evidence. In this study, we deal with optimizing medication and quantitatively present the role of deep learning in predicting the medication dosage for patients with Parkinson's disease (PD). The proposed method is based on recurrent neural networks (RNNs) and tries to predict the dosage of five critical medication types for PD, including levodopa, dopamine agonists, monoamine oxidase-B inhibitors, catechol-O-methyltransferase inhibitors, and amantadine. Recurrent neural networks have memory blocks that retain crucial information from previous patient visits. This feature is helpful for patients with PD, as the neurologist can refer to the patient's previous state and the prescribed medication to make informed decisions. We employed data from the Parkinson's Progression Markers Initiative. The dataset included information on the Unified Parkinson's Disease Rating Scale, Activities of Daily Living, Hoehn and Yahr scale, demographic details, and medication use logs for each patient. We evaluated several models, such as multi-layer perceptron (MLP), Simple-RNN, long short-term memory (LSTM), and gated recurrent units (GRU). Our analysis found that recurrent neural networks (LSTM and GRU) performed the best. More specifically, when using LSTM, we were able to predict levodopa and dopamine agonist dosage with a mean squared error of 0.009 and 0.003, mean absolute error of 0.062 and 0.030, root mean square error of 0.099 and 0.053, and R-squared of 0.514 and 0.711, respectively.

Multiresolution directed transfer function approach for segment-wise seizure classification of epileptic EEG signal

Currently, with the bloom in artificial intelligence (AI) algorithms, various human-centered smart systems can be utilized, especially in cognitive computing, for the detection of various chronic brain diseases such as epileptic seizure. The primary goal of this research article is to propose a novel human-centered cognitive computing (HCCC) method for segment-wise seizure classification by employing multiresolution extracted data with directed transfer function (DTF) features, termed as the multiresolution directed transfer function (MDTF) approach. Initially, the multiresolution information of the epileptic seizure signal is extracted using a multiresolution adaptive filtering (MRAF) method. These seizure details are passed to the DTF where the information flow of high frequency bands is computed. Thereafter, different measures of complexity such as approximate entropy (AEN) and sample entropy (SAEN) are computed from the extracted high frequency bands. Lastly, a k-nearest neighbor (k-NN) and support vector machine (SVM) are used for classifying the EEG signal into non-seizure and seizure data depending on the multiresolution based information flow characteristics. The MDTF approach is tested on a standard dataset and validated using a dataset from a local hospital. The proposed technique has obtained an average sensitivity of 98.31%, specificity of 96.13% and accuracy of 98.89% using SVM classifier. The average detection rate of the MDTF approach is 97.72% which is greater than the existing approaches. The proposed MDTF method will help neuro-specialists to locate seizure information drift which occurs within the consecutive segments and between two channels. The main advantage of the MDTF approach is its capability to locate the seizure activity contained by the EEG signal with accuracy. This will assist the neurologists with the precise localization of the epileptic seizure automatically and hence will reduce the burden of time-consuming epileptic seizure analysis.

Epilepsy classification using artificial intelligence: A web-based application

OBJECTIVE

The purpose of the current endeavor was to evaluate the feasibility of using easily accessible and applicable clinical information (based on history taking and physical examination) in order to make a reliable differentiation between idiopathic generalized epilepsy (IGE) versus focal epilepsy using machine learning (ML) methods.

METHODS

The first phase of the study was a retrospective study of a prospectively developed and maintained database. All patients with an electro-clinical diagnosis of IGE or focal epilepsy, at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2022, were included. The first author selected a set of clinical features. Using the stratified random portioning method, the dataset was divided into the train (70%) and test (30%) subsets. Different types of classifiers were assessed and the final classification was made based on their best results using the stacking method.

RESULTS

A total number of 1445 patients were studied; 964 with focal epilepsy and 481 with IGE. The stacking classifier led to better results than the base classifiers in general. This algorithm has the following characteristics: precision: 0.81, sensitivity: 0.81, and specificity: 0.77.

SIGNIFICANCE

We developed a pragmatic algorithm aimed at facilitating epilepsy classification for individuals whose epilepsy begins at age 10 years and older. Also, in order to enable and facilitate future external validation studies by other peers and professionals, the developed and trained ML model was implemented and published via an online web-based application that is freely available at http://www.epiclass.ir/f-ige.

Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram

Automatic seizure onset zone (SOZ) localization using interictal electrocorticogram (ECoG) improves the diagnosis and treatment of patients with medically refractory epilepsy. This study aimed to investigate the characteristics of phase-amplitude coupling (PAC) extracted from interictal ECoG and the feasibility of PAC serving as a promising biomarker for SOZ identification. We employed the mean vector length modulation index approach on the 20-s ECoG window to calculate PAC features between low-frequency rhythms (0.5-24 Hz) and high frequency oscillations (HFOs) (80-560 Hz). We used statistical measures to test the significant difference in PAC between the SOZ and non-seizure onset zone (NSOZ). To overcome the drawback of handcraft feature engineering, we established novel machine learning models to learn automatically the characteristics of the obtained PAC features and classify them to identify the SOZ. Besides, to handle imbalanced dataset classification, we introduced novel feature-wise/class-wise re-weighting strategies in conjunction with classifiers. In addition, we proposed a time-series nest cross-validation to provide more accurate and unbiased evaluations for this model. Seven patients with focal cortical dysplasia were included in this study. The experiment results not only showed that a significant coupling at band pairs of slow waves and HFOs exists in the SOZ when compared with the NSOZ, but also indicated the effectiveness of the PAC features and the proposed models in achieving better classification performance .

Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy

Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.

Automation: A revolutionary vision of artificial intelligence in theranostics

The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.

Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera

There is higher prevalence of epilepsy and SUDEP in people with intellectual disability (PwID) compared to general population. Accurate seizure recording particularly at night can be challenging in PwID. Neuro Event Labs seizure monitoring (Nelli) uses high-quality video based artificial intelligence to detect and record possible nocturnal seizures. This study looks to evaluate the clinical utility and acceptability of Nelli in PwID and epilepsy. Family/carers of PwID and drug resistant epilepsy with suspicions of nocturnal seizures who had not tolerated routine or ambulatory EEGs were invited to evaluate Nelli. Relevant demographics and clinical characteristics were collected. Nelli's impact, it's facilitators, barriers and feedback quality was captured from patient and professional stakeholders. Quantitative and thematic analysis was undertaken. Fifteen PwID and epilepsy and four health professionals were involved. Nelli recorded 707 possible seizure events across the study cohort of which 247 were not heard or recognised by carers. Carers recorded 165 episodes of 'restless' or "seizure behaviour" which the Nelli did not deem to be seizures. There was 93% acceptability. Thematic analysis revealed three broad themes of device acceptability, result implementation and possible seizure recognition ability. Nelli allowed for improved communication and care planning in a hitherto difficult to investigate population.

Temporal lobe epilepsy lateralisation and surgical outcome prediction using diffusion imaging

OBJECTIVE

We sought to augment the presurgical workup of medically refractory temporal lobe epilepsy by creating a supervised machine learning technique that uses diffusion-weighted imaging to classify patient-specific seizure onset laterality and surgical outcome.

METHODS

151 subjects were included in this analysis: 62 patients (aged 18-68 years, 36 women) and 89 healthy controls (aged 18-71 years, 47 women). We created a supervised machine learning technique that uses diffusion-weighted metrics to classify subject groups. Specifically, we sought to classify patients versus healthy controls, unilateral versus bilateral temporal lobe epilepsy, left versus right temporal lobe epilepsy and seizure-free versus not seizure-free surgical outcome. We then reduced the dimensionality of derived features with community detection for ease of interpretation.

RESULTS

We classified the subject groups in withheld testing data sets with a cross-fold average testing areas under the receiver operating characteristic curve of 0.745 for patients versus healthy controls, 1.000 for unilateral versus bilateral seizure onset, 0.662 for left versus right seizure onset, 0.800 for left-sided seizure-free vsersu not seizure-free surgical outcome and 0.775 for right-sided seizure-free versus not seizure-free surgical outcome.

CONCLUSIONS

This technique classifies important clinical decisions in the presurgical workup of temporal lobe epilepsy by generating discerning white-matter features. We believe that this work augments existing network connectivity findings in the field by further elucidating important white-matter pathology in temporal lobe epilepsy. We hope that this work contributes to recent efforts aimed at using diffusion imaging as an augmentation to the presurgical workup of this devastating neurological disorder.

Sage Against the Machine: Promise and Challenge of Artificial Intelligence in Epilepsy

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia

Gill RS, Lee HM, Caldairou B, et al. Neurology. 2021 Oct 19;97(16):e1571-e1582. doi:10.1212/WNL.0000000000012698. Epub 2021 Sep 14. PMID: 34521691; PMCID: PMC8548962.

Background and Objective:

To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD).

Methods:

We used clinically acquired 3-dimensional (3D) T1-weighted and 3D fluid-attenuated inversion recovery MRI of 148 patients (median age 23 years [range 2-55 years]; 47% female) with histologically verified FCD at 9 centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed MRI-negative in 51% of patients, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 cases with FCD (13 ± 10 years). Applying the algorithm to 42 healthy controls and 89 controls with temporal lobe epilepsy disease tested specificity.

Results:

Overall sensitivity was 93% (137 of 148 FCD detected) using a leave-one-site-out cross-validation, with an average of 6 false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half, it ranked the highest. Sensitivity in the independent cohort was 83% (19 of 23; average of 5 false positives per patient). Specificity was 89% in healthy and disease controls.

Discussion:

This first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification, this classifier may assist clinicians in adjusting hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for presurgical evaluation of MRI-negative epilepsy. Classification of evidence: This study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in patients with epilepsy initially diagnosed as MRI negative.

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

BACKGROUND

Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduce the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patients occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements in medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases, PubMed, Scopus and EMBASE.

METHODS

Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review.

RESULTS

These articles revealed ambulatory, non-invasive and wearable medical devices, such as the in-ear EEG devices; the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signalbased devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures.

CONCLUSION

The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).

Neuroimaging in the Era of Artificial Intelligence: Current Applications

BACKGROUND

Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI increases efficiency and reduces errors, making it a valuable resource for physicians. With the increasing amount of data processing and image interpretation required, the ability to use AI to augment and aid the radiologist could improve the quality of patient care.

OBSERVATIONS

AI can predict patient wait times, which may allow more efficient patient scheduling. Additionally, AI can save time for repeat magnetic resonance neuroimaging and reduce the time spent during imaging. AI has the ability to read computed tomography, magnetic resonance imaging, and positron emission tomography with reduced or without contrast without significant loss in sensitivity for detecting lesions. Neuroimaging does raise important ethical considerations and is subject to bias. It is vital that users understand the practical and ethical considerations of the technology.

CONCLUSIONS

The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI's use for detecting neurologic conditions holds promise in combatting ever increasing imaging volumes and providing timely diagnoses.

A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders

Simple Summary

This study represents a resourceful review article that can deliver resources on neurological diseases and their implemented classification algorithms to reveal the future direction of researchers. Researchers interested in studying neurological diseases and previously implemented techniques in this field can follow this article. Various challenges occur in detecting different stages of the disorders. A limited amount of labeled and unlabeled datasets and other limitations is represented in this article to assist them in finding out the directions. The authors’ purpose for composing this article is to make a straightforward and concrete path for researchers to quickly find the way and the scope in this field for implementing future research on neurological disease detection.

Abstract

Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.

Artificial Intelligence: Review of Current and Future Applications in Medicine

Background

The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development.

Observations

With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology.

Conclusions

AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.

Brain injuries can set up an epileptogenic neuronal network

Development of epilepsy or epileptogenesis promotes recurrent seizures. As of today, there are no effective prophylactic therapies to prevent the onset of epilepsy. Contributing to this deficiency of preventive therapy is the lack of clarity in fundamental neurobiological mechanisms underlying epileptogenesis and lack of reliable biomarkers to identify patients at risk for developing epilepsy. This limits the development of prophylactic therapies in epilepsy. Here, neural network dysfunctions reflected by oscillopathies and microepileptiform activities, including neuronal hyperexcitability and hypersynchrony, drawn from both clinical and experimental epilepsy models, have been reviewed. This review suggests that epileptogenesis reflects a progressive and dynamic dysfunction of specific neuronal networks which recruit further interconnected groups of neurons, with this resultant pathological network mediating seizure occurrence, recurrence, and progression. In the future, combining spatial and temporal resolution of neuronal non-invasive recordings from patients at risk of developing epilepsy, together with analytics and computational tools, may contribute to determining whether the brain is undergoing epileptogenesis in asymptomatic patients following brain injury.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Survey on Antiepileptic Drug Therapy in Patients with Drug Resistant Epilepsy

Background and Purpose

Individualized anti-epileptic drug (AED) selection in patient with epilepsy is crucial. However, there is no unified opinion in treating patients with drug resistant epilepsy (DRE). This survey aimed to make a consolidate consensus with epileptologists’ perspectives of the treatment for Korean DRE patients by survey responses.

Methods

The survey was conducted with Korean epilepsy experts who have experience prescribing AEDs via e-mail. Survey questionnaires consisted of six items regarding prescription patterns and practical questions in treating patients with DRE in Korea. The research period was from February 2021 to March 2021.

Results

The survey response rate was 83.3% (90/108). Most (77.8%) of the responders are neurologists. The proportion of patients whose seizures were not controlled by the second AED was 26.9%. The proportion of patients who had taken five or more AEDs is 13.9%, and those who are currently taking five or more AEDs are 7.3%, of which 54.5% and 37.9% reported positive effects on additional AED, respectively. The majority (91.1%) of respondents answered that the mechanism of action was the top priority factor when adding AED. Regarding data priority, responders considered that expert opinion should have the top priority, followed by clinical experiences, reimbursement guidelines and clinical evidence. Responders gave 64.9 points (range from 0 to 100) about overall satisfaction on reimbursement system of Health Insurance Review and Assessment Service for AED.

Conclusions

This study on AED therapy for DRE patients is the first nationwide trial in Korean epilepsy experts. In five drug failure, the top priorities on AED selection are mechanism of action and expert opinion. These findings might help to achieve consensus and recognize the insight on optimal therapy of AED in DRE.

Prediction of Seizure Recurrence. A Note of Caution

Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.

Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls

To investigative whether radiomics features in bilateral hippocampi from MRI can identify temporal lobe epilepsy (TLE). A total of 131 subjects with MRI (66 TLE patients [35 right and 31 left TLE] and 65 healthy controls [HC]) were allocated to training (n = 90) and test (n = 41) sets. Radiomics features (n = 186) from the bilateral hippocampi were extracted from T1-weighted images. After feature selection, machine learning models were trained. The performance of the classifier was validated in the test set to differentiate TLE from HC and ipsilateral TLE from HC. Identical processes were performed to differentiate right TLE from HC (training set, n = 69; test set; n = 31) and left TLE from HC (training set, n = 66; test set, n = 30). The best-performing model for identifying TLE showed an AUC, accuracy, sensitivity, and specificity of 0.848, 84.8%, 76.2%, and 75.0% in the test set, respectively. The best-performing radiomics models for identifying right TLE and left TLE subgroups showed AUCs of 0.845 and 0.840 in the test set, respectively. In addition, multiple radiomics features significantly correlated with neuropsychological test scores (false discovery rate-corrected p-values < 0.05). The radiomics model from hippocampus can be a potential biomarker for identifying TLE.